The former AWS architect building fintech for people banks still overlook

London-based Tomi Popoola is building at the sharp end of fintech, AI and inclusion, using her work in cloud infrastructure and financial technology to widen access for people still left outside the system.

Tomi Popoola’s career sits where some of the biggest questions in business now collide: AI, money, cloud infrastructure and who actually gets access to opportunity.

The founder and CEO of Slash Finances, Tomi is a former AWS Solutions Architect with experience across financial technology, cloud-native systems and large-scale digital infrastructure. She began her career at JP Morgan before moving into cloud and fintech, later building Slash Finances around financial inclusion and accessible technology.

Her work also carries a wider leadership message. As a DEI advocate in technology, Tomi speaks on financial inclusion, generative AI, women in tech, cloud strategy and how underrepresented communities can move from being discussed in innovation conversations to shaping them.

In this exclusive interview with the Inspirational Leadership Speakers Agency, Tomi discusses AI, fintech, inclusive leadership and why the future of technology depends on building systems that work for more than the people already inside them.

1. Which layers of the AI-fintech stack will capture the most strategic leverage over the next 12 to 18 months?

Tomi Popoola: Strategic leverage will concentrate where AI meets irreplaceable structural advantages, and three layers stand out.

The first is proprietary data. The durable moat in financial AI is not the model. It is the underlying transaction and behavioural data that trains and continuously refines it. Banks, payment networks and large fintech platforms already hold datasets that compound in value as AI becomes more deeply embedded in operations. AI can commoditise models, but it cannot commoditise the data feeding them.

The second is fraud and risk infrastructure. This layer will monetise fastest because the return on investment is immediate and measurable. Even marginal improvements in fraud detection translate directly into loss reduction, while better risk scoring expands approvals without inflating defaults. These tools also slot into existing workflows with relatively low friction, which accelerates adoption.

The third, and most durable, is distribution. The greatest value in financial services has always accrued to whoever owns the customer relationship. Payments apps, neobanks and embedded finance platforms can deploy AI across lending, insurance, payments and financial advice, compounding its impact across the entire customer lifecycle.

2. What metrics should investors track to assess whether AI is genuinely moving the needle?

Tomi Popoola: The most revealing metrics are those where machine intelligence should produce outcomes that humans or rules-based systems simply cannot match at scale.

Fraud loss rate is often the cleanest signal of model effectiveness. Look for sustained reductions without a corresponding spike in false positives that reject legitimate transactions.

Cost-to-serve per account captures the operational impact of automation across support, compliance and underwriting. If AI is working, this number should fall without a degradation in service quality.

Credit approval rates alongside default metrics are particularly instructive together. Rising approvals paired with stable or declining charge-offs suggest better risk discrimination, not just looser standards.

CAC-to-LTV ratios can improve meaningfully if AI enables more precise targeting, better product-market fit at the customer level and higher cross-sell conversion.

Manual review rates are perhaps the most underused metric. The proportion of transactions still requiring human intervention in fraud, compliance or underwriting is a direct measure of automation effectiveness. Consistent declines here translate almost mechanically into margin expansion.

3. What near-term catalysts could shift industry sentiment around AI adoption in finance?

Tomi Popoola: Three developments over the next two quarters could meaningfully change how quickly institutions move from experimentation to scaled deployment.

Regulatory clarity is likely the most consequential. Globally, financial regulators are still defining acceptable frameworks for model governance, explainability and accountability. Clear guidance, particularly around automated lending decisions and explainable AI requirements, would remove one of the most significant sources of institutional hesitation. Uncertainty, not scepticism, is often what stalls deployment.

Fraud dynamics could become a defining proving ground. Large platforms demonstrating measurable, auditable reductions in fraud losses would do more to validate AI’s operational value than almost any other data point. The risk cuts both ways, though. A wave of AI-enabled fraud attacks that outpace existing defences could trigger a defensive spending surge that reshapes investment priorities across the sector almost overnight.

Demonstrated operational savings at major institutions would signal that AI has crossed from pilot to infrastructure. Watch for declining manual operations headcount, reduced call-centre volumes and automated compliance workflows appearing in earnings commentary. These are the tangible indicators that efficiency gains are compounding at scale.

This exclusive interview with Tomi Popoola was conducted by Tabish Ali of the Motivational Speakers Agency.